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at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["# 指定当前的工作文件夹\n","import os\n","# 此处为google drive中的文件路径,drive为之前指定的工作根目录，要加上\n","os.chdir(\"/content/drive/MyDrive/HW3/\")\n"],"metadata":{"id":"SNACRwa4i9UC","executionInfo":{"status":"ok","timestamp":1734578839728,"user_tz":-480,"elapsed":857,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# Download Link\n","# Link 1 (Dropbox): https://www.dropbox.com/s/up5q1gthsz3v0dq/food-11.zip?dl=0\n","# Link 2 (Google Drive): https://drive.google.com/file/d/1tbGNwk1yGoCBdu4Gi_Cia7EJ9OhubYD9/view?usp=share_link\n","# Link 3: Kaggle Competition.\n","\n","# (1) dropbox link\n","!wget -O food11.zip https://www.dropbox.com/s/up5q1gthsz3v0dq/food-11.zip?dl=0\n","\n","# (2) google drive link\n","# !gdown --id '1tbGNwk1yGoCBdu4Gi_Cia7EJ9OhubYD9' --output food11.zip\n","! unzip 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random"],"metadata":{"id":"TqgI1rQCr1oo","executionInfo":{"status":"ok","timestamp":1734578680455,"user_tz":-480,"elapsed":9166,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["myseed = 6666  # set a random seed for reproducibility\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(myseed)\n","torch.manual_seed(myseed)\n","if torch.cuda.is_available():\n","    torch.cuda.manual_seed_all(myseed)"],"metadata":{"id":"LRnzXstjr3MK","executionInfo":{"status":"ok","timestamp":1734578685115,"user_tz":-480,"elapsed":1060,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["# Normally, We don't need augmentations in testing and validation.\n","# All we need here is to resize the PIL image and transform it into Tensor.\n","test_tfm = transforms.Compose([\n","    transforms.Resize((128, 128)),\n","    transforms.ToTensor(),\n","])\n","\n","# However, it is also possible to use augmentation in the testing phase.\n","# You may use train_tfm to produce a variety of images and then test using ensemble methods\n","train_tfm = transforms.Compose([\n","    # Resize the image into a fixed shape (height = width = 128)\n","    transforms.Resize((128, 128)),\n","    # You may add some transforms here.\n","\n","    # ToTensor() should be the last one of the transforms.\n","    transforms.ToTensor(),\n","])\n"],"metadata":{"id":"FhiiHaDLucp8","executionInfo":{"status":"ok","timestamp":1734578998098,"user_tz":-480,"elapsed":2,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["class FoodDataset(Dataset):\n","\n","    def __init__(self,path,tfm=test_tfm,files = None):\n","        super(FoodDataset).__init__()\n","        self.path = path\n","        self.files = sorted([os.path.join(path,x) for x in os.listdir(path) if x.endswith(\".jpg\")])\n","        if files != None:\n","            self.files = files\n","\n","        self.transform = tfm\n","\n","    def __len__(self):\n","        return len(self.files)\n","\n","    def __getitem__(self,idx):\n","        fname = self.files[idx]\n","        im = Image.open(fname)\n","        im = self.transform(im)\n","\n","        try:\n","            label = int(fname.split(\"/\")[-1].split(\"_\")[0])\n","        except:\n","            label = -1 # test has no label\n","\n","        return im,label"],"metadata":{"id":"mzNCA4QFvJ5o","executionInfo":{"status":"ok","timestamp":1734579001715,"user_tz":-480,"elapsed":720,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["class Classifier(nn.Module):\n","    def __init__(self):\n","        super(Classifier, self).__init__()\n","        # torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)\n","        # torch.nn.MaxPool2d(kernel_size, stride, padding)\n","        # input 維度 [3, 128, 128]\n","        self.cnn = nn.Sequential(\n","            nn.Conv2d(3, 64, 3, 1, 1),  # [64, 128, 128]\n","            nn.BatchNorm2d(64),\n","            nn.ReLU(),\n","            nn.MaxPool2d(2, 2, 0),      # [64, 64, 64]\n","\n","            nn.Conv2d(64, 128, 3, 1, 1), # [128, 64, 64]\n","            nn.BatchNorm2d(128),\n","            nn.ReLU(),\n","            nn.MaxPool2d(2, 2, 0),      # [128, 32, 32]\n","\n","            nn.Conv2d(128, 256, 3, 1, 1), # [256, 32, 32]\n","            nn.BatchNorm2d(256),\n","            nn.ReLU(),\n","            nn.MaxPool2d(2, 2, 0),      # [256, 16, 16]\n","\n","            nn.Conv2d(256, 512, 3, 1, 1), # [512, 16, 16]\n","            nn.BatchNorm2d(512),\n","            nn.ReLU(),\n","            nn.MaxPool2d(2, 2, 0),       # [512, 8, 8]\n","\n","            nn.Conv2d(512, 512, 3, 1, 1), # [512, 8, 8]\n","            nn.BatchNorm2d(512),\n","            nn.ReLU(),\n","            nn.MaxPool2d(2, 2, 0),       # [512, 4, 4]\n","        )\n","        self.fc = nn.Sequential(\n","            nn.Linear(512*4*4, 1024),\n","            nn.ReLU(),\n","            nn.Linear(1024, 512),\n","            nn.ReLU(),\n","            nn.Linear(512, 11)\n","        )\n","    def forward(self, x):\n","        out = self.cnn(x)\n","        out = out.view(out.size()[0], -1)\n","        return self.fc(out)"],"metadata":{"id":"w8ZjkkGzvP2p","executionInfo":{"status":"ok","timestamp":1734579003780,"user_tz":-480,"elapsed":3,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["# \"cuda\" only when GPUs are available.\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","\n","# Initialize a model, and put it on the device specified.\n","model = Classifier().to(device)\n","\n","# The number of batch size.\n","batch_size = 64\n","\n","# The number of training epochs.\n","n_epochs = 8\n","\n","# If no improvement in 'patience' epochs, early stop.\n","patience = 5\n","\n","# For the classification task, we use cross-entropy as the measurement of performance.\n","criterion = nn.CrossEntropyLoss()\n","\n","# Initialize optimizer, you may fine-tune some hyperparameters such as learning rate on your own.\n","optimizer = torch.optim.Adam(model.parameters(), lr=0.0003, weight_decay=1e-5)"],"metadata":{"id":"NL5z1bTivhQa","executionInfo":{"status":"ok","timestamp":1734579008008,"user_tz":-480,"elapsed":681,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["# Construct train and valid datasets.\n","# The argument \"loader\" tells how torchvision reads the data.\n","train_set = FoodDataset(\"./train\", tfm=train_tfm)\n","train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True)\n","valid_set = FoodDataset(\"./valid\", tfm=test_tfm)\n","valid_loader = DataLoader(valid_set, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True)"],"metadata":{"id":"dqJPSzXpvjH1","executionInfo":{"status":"ok","timestamp":1734579011856,"user_tz":-480,"elapsed":1742,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["# Initialize trackers, these are not parameters and should not be changed\n","stale = 0\n","best_acc = 0\n","\n","for epoch in range(n_epochs):\n","\n","    # ---------- Training ----------\n","    # Make sure the model is in train mode before training.\n","    model.train()\n","\n","    # These are used to record information in training.\n","    train_loss = []\n","    train_accs = []\n","\n","    for batch in tqdm(train_loader):\n","\n","        # A batch consists of image data and corresponding labels.\n","        imgs, labels = batch\n","        #imgs = imgs.half()\n","        #print(imgs.shape,labels.shape)\n","\n","        # Forward the data. (Make sure data and model are on the same device.)\n","        logits = model(imgs.to(device))\n","\n","        # Calculate the cross-entropy loss.\n","        # We don't need to apply softmax before computing cross-entropy as it is done automatically.\n","        loss = criterion(logits, labels.to(device))\n","\n","        # Gradients stored in the parameters in the previous step should be cleared out first.\n","        optimizer.zero_grad()\n","\n","        # Compute the gradients for parameters.\n","        loss.backward()\n","\n","        # Clip the gradient norms for stable training.\n","        grad_norm = nn.utils.clip_grad_norm_(model.parameters(), max_norm=10)\n","\n","        # Update the parameters with computed gradients.\n","        optimizer.step()\n","\n","        # Compute the accuracy for current batch.\n","        acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()\n","\n","        # Record the loss and accuracy.\n","        train_loss.append(loss.item())\n","        train_accs.append(acc)\n","\n","    train_loss = sum(train_loss) / len(train_loss)\n","    train_acc = sum(train_accs) / len(train_accs)\n","\n","    # Print the information.\n","    print(f\"[ Train | {epoch + 1:03d}/{n_epochs:03d} ] loss = {train_loss:.5f}, acc = {train_acc:.5f}\")\n","\n","    # ---------- Validation ----------\n","    # Make sure the model is in eval mode so that some modules like dropout are disabled and work normally.\n","    model.eval()\n","\n","    # These are used to record information in validation.\n","    valid_loss = []\n","    valid_accs = []\n","\n","    # Iterate the validation set by batches.\n","    for batch in tqdm(valid_loader):\n","\n","        # A batch consists of image data and corresponding labels.\n","        imgs, labels = batch\n","        #imgs = imgs.half()\n","\n","        # We don't need gradient in validation.\n","        # Using torch.no_grad() accelerates the forward process.\n","        with torch.no_grad():\n","            logits = model(imgs.to(device))\n","\n","        # We can still compute the loss (but not the gradient).\n","        loss = criterion(logits, labels.to(device))\n","\n","        # Compute the accuracy for current batch.\n","        acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()\n","\n","        # Record the loss and accuracy.\n","        valid_loss.append(loss.item())\n","        valid_accs.append(acc)\n","        #break\n","\n","    # The average loss and accuracy for entire validation set is the average of the recorded values.\n","    valid_loss = sum(valid_loss) / len(valid_loss)\n","    valid_acc = sum(valid_accs) / len(valid_accs)\n","\n","    # Print the information.\n","    print(f\"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}\")\n","\n","\n","    # update logs\n","    if valid_acc > best_acc:\n","        with open(f\"./{_exp_name}_log.txt\",\"a\"):\n","            print(f\"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f} -> best\")\n","    else:\n","        with open(f\"./{_exp_name}_log.txt\",\"a\"):\n","            print(f\"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}\")\n","\n","\n","    # save models\n","    if valid_acc > best_acc:\n","        print(f\"Best model found at epoch {epoch}, saving model\")\n","        torch.save(model.state_dict(), f\"{_exp_name}_best.ckpt\") # only save best to prevent output memory exceed error\n","        best_acc = valid_acc\n","        stale = 0\n","    else:\n","        stale += 1\n","        if stale > patience:\n","            print(f\"No improvment {patience} consecutive epochs, early stopping\")\n","            break"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":388,"referenced_widgets":["6ad31534c4124d27b09a8ef3d0660a85","bc78dbc9d1f143b488545d75a02669cf","a8a8aa4d54c349e8bef201cd03210363","4fcb9e76ff044845943d707f72d4030d","a8031c8129a345e78560158d6baa9e31","23dc2fecd376435c969035bf1d9b95c6","f5bca968bbbd4c47a8ae816572960ee4","320a5d21ddd546f4889ddc71216f2a18","2c6bfbf7871d427d923cc04075345f66","8878ddca2d834a92b5b0f22c52d7f831","445c096714a54b71b3a89d8beff04f95"]},"id":"Z9ceQQTIvlJQ","executionInfo":{"status":"error","timestamp":1734579021444,"user_tz":-480,"elapsed":7961,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}},"outputId":"9a341e3e-8a4b-46b4-d54e-a469d8eb7ea0"},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/157 [00:00<?, 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250\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m                 \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1181\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1182\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1183\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    699\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             if (\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    755\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 757\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    758\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    759\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     50\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     53\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     50\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode, formats)\u001b[0m\n\u001b[1;32m   3478\u001b[0m         \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3480\u001b[0;31m     \u001b[0mprefix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3482\u001b[0m     \u001b[0mpreinit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["# Construct test datasets.\n","# The argument \"loader\" tells how torchvision reads the data.\n","test_set = FoodDataset(\"./test\", tfm=test_tfm)\n","test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True)"],"metadata":{"id":"t89d4H_T5asw","executionInfo":{"status":"ok","timestamp":1734579036636,"user_tz":-480,"elapsed":984,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["model_best = Classifier().to(device)\n","model_best.load_state_dict(torch.load(f\"{_exp_name}_best.ckpt\"))\n","model_best.eval()\n","prediction = []\n","with torch.no_grad():\n","    for data,_ in tqdm(test_loader):\n","        test_pred = model_best(data.to(device))\n","        test_label = np.argmax(test_pred.cpu().data.numpy(), axis=1)\n","        prediction += test_label.squeeze().tolist()"],"metadata":{"id":"Txfpsp-b5dmT","colab":{"base_uri":"https://localhost:8080/","height":104,"referenced_widgets":["328510861f204366a66d26736ac2acba","44960b4b39db4b07b89bf682cfcb8e9e","ea462fa277cb4ef89509781042123848","1b6f53c5ebf849c3a604796433efad1d","9ddab0d04f8149a79af0f6eb86ae54af","c0283964e9a0459d8d797d2b9784e6d4","40922851b35343159824ac4ac30f9e04","a8fcd39adab04ae0b5f804dad8334601","aa3ae8ff5c0146e088cea07283e1f3ae","4d2c2644ed3f473495e579d38d7373a8","da5007744bc64a3b9aaddafb271f1493"]},"executionInfo":{"status":"ok","timestamp":1734579159326,"user_tz":-480,"elapsed":98800,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}},"outputId":"4bc7714e-725b-4e27-c0b9-0a871da0fa82"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-16-b23ababdbf1e>:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n","  model_best.load_state_dict(torch.load(f\"{_exp_name}_best.ckpt\"))\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/47 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"328510861f204366a66d26736ac2acba"}},"metadata":{}}]},{"cell_type":"code","source":["# create test csv\n","def pad4(i):\n","    return \"0\"*(4-len(str(i)))+str(i)\n","df = pd.DataFrame()\n","df[\"Id\"] = [pad4(i) for i in range(len(test_set))]\n","df[\"Category\"] = prediction\n","df.to_csv(\"submission.csv\",index = False)"],"metadata":{"id":"HVjyRyJJ5fIU","executionInfo":{"status":"ok","timestamp":1734579674785,"user_tz":-480,"elapsed":2136,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}}},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":["Q1.Augmentation Implementation"],"metadata":{"id":"bhlVbd89hNgb"}},{"cell_type":"code","source":["train_tfm = transforms.Compose([\n","    # Resize the image into a fixed shape (height = width = 128)\n","    transforms.Resize((128, 128)),\n","    # You can add some transforms here.\n","    transforms.ToTensor(),\n","])"],"metadata":{"id":"tG7RED5XhUZu","executionInfo":{"status":"ok","timestamp":1734579680253,"user_tz":-480,"elapsed":541,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}}},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":["Q2.Visual Representations Implementation"],"metadata":{"id":"cumasb5ehWv5"}},{"cell_type":"code","source":["import torch\n","import numpy as np\n","from sklearn.manifold import TSNE\n","import matplotlib.pyplot as plt\n","from tqdm import tqdm\n","import matplotlib.cm as cm\n","import torch.nn as nn\n","\n","device = 'cuda' if torch.cuda.is_available() else 'cpu'\n","\n","# Load the trained model\n","model = Classifier().to(device)\n","state_dict = torch.load(f\"{_exp_name}_best.ckpt\")\n","model.load_state_dict(state_dict)\n","model.eval()\n","\n","print(model)"],"metadata":{"id":"mEKB7mB7hgFk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1734579685400,"user_tz":-480,"elapsed":3072,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}},"outputId":"ea6dffc8-5100-4c11-9816-e99806bb0862"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Classifier(\n","  (cnn): Sequential(\n","    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (2): ReLU()\n","    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (5): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (6): ReLU()\n","    (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (8): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (9): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (10): ReLU()\n","    (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (12): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (13): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (14): ReLU()\n","    (15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (17): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (18): ReLU()\n","    (19): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  )\n","  (fc): Sequential(\n","    (0): Linear(in_features=8192, out_features=1024, bias=True)\n","    (1): ReLU()\n","    (2): Linear(in_features=1024, out_features=512, bias=True)\n","    (3): ReLU()\n","    (4): Linear(in_features=512, out_features=11, bias=True)\n","  )\n",")\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-19-6706b174c0ef>:13: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n","  state_dict = torch.load(f\"{_exp_name}_best.ckpt\")\n"]}]},{"cell_type":"code","source":["# Load the vaildation set defined by TA\n","valid_set = FoodDataset(\"./valid\", tfm=test_tfm)\n","valid_loader = DataLoader(valid_set, batch_size=64, shuffle=False, num_workers=0, pin_memory=True)\n","\n","# Extract the representations for the specific layer of model\n","index = 10 # You should find out the index of layer which is defined as \"top\" or 'mid' layer of your model.\n","features = []\n","labels = []\n","for batch in tqdm(valid_loader):\n","    imgs, lbls = batch\n","    with torch.no_grad():\n","        logits = model.cnn[:index](imgs.to(device))\n","        logits = logits.view(logits.size()[0], -1)\n","    labels.extend(lbls.cpu().numpy())\n","    logits = np.squeeze(logits.cpu().numpy())\n","    features.extend(logits)\n","\n","features = np.array(features)\n","colors_per_class = cm.rainbow(np.linspace(0, 1, 11))\n","\n","# Apply t-SNE to the features\n","features_tsne = TSNE(n_components=2, init='pca', random_state=42).fit_transform(features)\n","\n","# Plot the t-SNE visualization\n","plt.figure(figsize=(10, 8))\n","for label in np.unique(labels):\n","    plt.scatter(features_tsne[labels == label, 0], features_tsne[labels == label, 1], label=label, s=5)\n","plt.legend()\n","plt.show()"],"metadata":{"id":"IzP6JPimhrXy","colab":{"base_uri":"https://localhost:8080/","height":693},"executionInfo":{"status":"ok","timestamp":1734581274632,"user_tz":-480,"elapsed":415466,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}},"outputId":"f236911e-fee8-49b7-8f19-f85f95e408e3"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 57/57 [01:08<00:00,  1.21s/it]\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x800 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["在深度学习中，不同层学习到的特征具有不同的抽象程度。通常来说，较靠近输入层的层学习到的是低级特征（如边缘、纹理等），而较靠近输出层的层学习到的是高级特征（如物体部分、整体物体等）。第 10 层、（索引为 9）是一个包含 Conv2d(128, 256) 的层。这一层可能学习到了一些中等级别的特征，介于低级特征和高级特征之间。选择这一层的特征可能有助于在特征空间中找到对输入图像进行分类有意义的表示。"],"metadata":{"id":"coyUuwk3EK_L"}},{"cell_type":"code","source":["print(model.cnn)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b-v9O_gBD8QZ","executionInfo":{"status":"ok","timestamp":1734580621279,"user_tz":-480,"elapsed":508,"user":{"displayName":"唐心茹","userId":"05716964743246501119"}},"outputId":"6666153d-818e-4942-d225-ff8d6f87fec5"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["Sequential(\n","  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (2): ReLU()\n","  (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (5): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (6): ReLU()\n","  (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (8): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (9): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (10): ReLU()\n","  (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (12): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (13): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (14): ReLU()\n","  (15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (17): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (18): ReLU()\n","  (19): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",")\n"]}]}]}